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Generative Artificial Intelligence in Anatomic Pathology
Victor Brodsky
, Ehsan Ullah
, Andrey Bychkov
, Andrew H. Song
, Eric E. Walk
, Peter Louis
, Ghulam Rasool
, Rajendra S. Singh
, Faisal Mahmood
, Marilyn M. Bui
, Anil V. Parwani
Division of Anatomic and Molecular Pathology (AMP)
Research output
:
Contribution to journal
›
Article
›
peer-review
16
Link opens in a new tab
Scopus citations
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Keyphrases
Anatomic Pathology
100%
Generative Artificial Intelligence
100%
Artificial Intelligence
66%
Workflow Efficiency
66%
Diagnostic Accuracy
50%
Image Analysis
33%
Synthetic Data Generation
33%
Patient Care
16%
Recent Advancements
16%
Task Success
16%
Clinical Practice
16%
Clinical Utility
16%
High Standards
16%
Nondiagnostic
16%
Interdisciplinary Collaboration
16%
Academic Setting
16%
Educational Content
16%
Diagnostic Process
16%
Quality Control Tests
16%
Reflex Testing
16%
Data Analysis Methods
16%
Ethical Considerations
16%
Ethical Standards
16%
Artificial Intelligence Applications
16%
Successful Integration
16%
Federated Learning
16%
Rigorous Validation
16%
Clinical Integration
16%
Learning Data
16%
Research Capability
16%
Histology Images
16%
Virtual Staining
16%
Advanced Data Analysis
16%
Prompt Engineering
16%
Business Workflow
16%
Routine Tasks
16%
Multimodal Applications
16%
Cautious Optimism
16%
Computer Science
Generative Artificial Intelligence
100%
Artificial Intelligence
66%
Synthetic Data Generation
33%
Image Analysis
16%
Clinical Utility
16%
Data Source
16%
Academic Setting
16%
Ethical Consideration
16%
Prompt Engineering
16%
Artificial Intelligence Applications
16%
Diagnostic Process
16%
Ethical Standard
16%
Preliminary Survey
16%
Quality Control
16%
Federated machine learning
16%
Medicine and Dentistry
Anatomical Pathology
100%
Artificial Intelligence
100%
Diagnostic Accuracy
27%
Patient Care
9%
Image Analysis
9%
Federated machine learning
9%
Neuroscience
Anatomical Pathology
100%
Staining Technique
16%
Federated machine learning
16%
Psychology
Artificial Intelligence
100%
Clinical Utility
9%